A Distance-Based Decision in the Credal Level

Belief function theory provides a flexible way to combine information provided by different sources. This combination is usually followed by a decision making which can be handled by a range of decision rules. Some rules help to choose the most likely hypothesis. Others allow that a decision is made on a set of hypotheses. In [6], we proposed a decision rule based on a distance measure. First, in this paper, we aim to demonstrate that our proposed decision rule is a particular case of the rule proposed in [4]. Second, we give experiments showing that our rule is able to decide on a set of hypotheses. Some experiments are handled on a set of mass functions generated randomly, others on real databases

Case-based Reasoning for Knowledge Capitalization in Inventive Design Using Latent Semantic Analysis

International audienceNowadays, innovation represents one of the most crucial factors driving the success of companies. The Theory of Inventive Problem Solving (also known as TRIZ) is a well-established method to facilitate systematic inventive design. Although, TRIZ allows solving inventive problems through a panoply of knowledge sources, it may make inventive problem solving a time-consuming, experience demanding process and lead to waste of resources of the companies. To avoid the use of these tools and to help new users in solving their inventive problems without completely mastering TRIZ, we propose in this paper an approach based on the use of the Case-based reasoning (CBR) in order to capitalize experience. CBR is a knowledge paradigm that solves a new problem by finding the old similar cases and reusing them. The retrieval is conducted in order to find the old similar cases, and the old solutions of the retrieved cases are adapted to solve the new problem. In this paper, a systematic three-level adaptation is proposed to reduce the effort required of the users in choosing the suitable solution to solve their problem. An example is used to illustrate in detail the proposed approach

Gestion du conflit dans l'appariement des ontologies

National audienceMapping ontologies is a crucial step to facilitate semantic interoperability between systems. Different matchers can be used in order to find the correspondences between two ontologies. These matchers can be contradictory, thus leading to a conflict. In order to manage this conflict, we propose an approach by using the Dempster-Shafer theory. Every matcher is considered as a source of evidence and the match results as the mass functions. Managing conflict is dealt when combining between the different results using for that purpose different rules of combination

A distance-based decision in the credal level

Abstract. Belief function theory provides a flexible way to combine information provided by different sources. This combination is usually followed by a decision making which can be handled by a range of decision rules. Some rules help to choose the most likely hypothesis. Others allow that a decision is made on a set of hypotheses. In [6], we proposed a decision rule based on a distance measure. First, in this paper, we aim to demonstrate that our proposed decision rule is a particular case of the rule proposed i

Prise de décision pour l'appariement d'ontologies avec la théorie des fonctions de croyance

Ontology matching is a solution to mitigate the effect of semantic heterogeneity. Matching techniques, based on similarity measures, are used to find correspondences between ontologies. Using a unique similarity measure does not guarantee a perfect alignment. For that reason, it is necessary to use more than a similarity measure to take advantage of features of each one and then to combine the different outcomes. In this thesis, we proposea credibilistic decision process by using the theory of belief functions. First, we model the alignments, obtained after a matching process, under the theory of belief functions. Then, we combine the different outcomes through using adequate combination rules. Due to our awareness that making decision is a crucial step in any process and that most of the decision rules of the belief function theory are able to give results on a unique element,we propose a decision rule based on a distance measure able to make decision on union of elements (i.e. to identify for each source entity its corresponding target entities).L’appariement d’ontologies est une tâche primordiale pour pallier le problème de l’hétérogénéité sémantique et ainsi assurer une interopérabilité entre les applications utilisant différentes ontologies. Il consiste en la mise en correspondance de chaque entité d’une ontologie source à une entité d’une ontologie cible et ceci par application de techniques d’alignement fondées sur des mesures de similarité. Individuellement, aucune mesure de similarité ne permet d’obtenir un alignement parfait. C’est pour cette raison qu’il est intéressant de tenir compte de la complémentarité des mesures afin d’obtenir un meilleur alignement. Dans cette thèse, nous nous sommes intéressés à proposer un processus de décision crédibiliste pour l’appariement d’ontologies. Etant données deux ontologies, on procède à leur appariement et ceci par application de trois techniques. L’ensemble des alignements obtenus sera modélisé dans le cadre de la théorie des fonctions de croyance. Des règles de combinaison seront utilisées pour combiner les résultats d’alignement. Une étape de prise de décision s’avère utile, pour cette raison, nous proposons une règle de décision fondée surune distance et capable de décider sur une union d’hypothèses. Cette règle sera utilisée dans notre processsus afin d’identifier pour chaque entité source le ou les entités cibles

Prise de décision lors de l'appariement des ontologies dans le cadre de la théorie des fonctions de croyance

L'appariement des ontologies est une tâche primordiale pour palier au problème d'hétérogénéité sémantique et ainsi assurer une interopérabilité entre les applications utilisant différentes ontologies. Il consiste en la mise en correspondance de chaque entité d'une ontologie source à une entité d'une ontologie cible et ceci par application des techniques d'alignement fondées sur des mesures de similarité. Individuellement, aucune mesure de similarité ne permet d'obtenir un alignement parfait. C'est pour cette raison qu'il est intéressant de tenir compte de la complémentarité des mesures afin d'obtenir un meilleur alignement. Dans cette thèse, nous nous sommes intéressés à proposer un processus de décision crédibiliste pour l'appariement des ontologies. Étant données deux ontologies, on procède à leur appariement et ceci par application de trois techniques. Les alignements obtenus seront modélisés dans le cadre de la théorie des fonctions de croyance. Des règles de combinaison seront utilisées pour combiner les résultats d'alignement. Une étape de prise de décision s'avère utile, pour cette raison nous proposons une règle de décision fondée sur une distance et capable de décider sur une union d'hypothèses. Cette règle sera utilisée dans notre processus afin d'identifier pour chaque entité source le ou les entités cibles.Ontology matching is a solution to mitigate the effect of semantic heterogeneity. Matching techniques, based on similarity measures, are used to find correspondences between ontologies. Using a unique similarity measure does not guarantee a perfect alignment. For that reason, it is necessary to use more than a similarity measure to take advantage of features of each one and then to combine the different outcomes. In this thesis, we propose a credibilistic decision process by using the theory of belief functions. First, we model the alignments, obtained after a matching process, under the theory of belief functions. Then, we combine the different outcomes through using adequate combination rules. Due to our awareness that making decision is a crucial step in any process and that most of the decision rules of the belief function theory are able to give results on a unique element, we propose a decision rule based on a distance measure able to make decision on union of elements (i.e. to identify for each source entity its corresponding target entities)

Experience capitalization to support decision making in inventive problem solving

International audienceThe theory of inventive problem solving (TRIZ) is based on the use of tools and their related knowledge sources for solving different types of inventive problems. Using classical TRIZ tools to solve a specific problem requires additional knowledge such as the expert's accumulated know-how in their problem solving practice (i.e. experience). In order to facilitate the use of experience, this paper explores a new inventive problem solving approach based on experience capitalization. Accordingly, the user with less expertise can solve new problems by reusing or revising the old solutions of other people. This approach is based on the use of the case-based reasoning (CBR) for collecting and rapidly accessing the experiences. A case study illustrates the problem solving process based on the proposed approach. In addition to that, we conducted a set of experiments to evaluate our approach. We considered a number of new problems whose resolution is performed in three different ways: manually, using an existing rule-based approach and using our proposed approach. The evaluation demonstrates that our approach gives better results and improves decision making in the inventive design process